孟加拉语医疗问答系统BanglaMed-QA上线，解决低资源语言医疗信息缺口，准确率达95%。
BanglaMed-QA是专为孟加拉语医疗领域设计的问答系统，包含4,493个问答对，覆盖506种疾病。该系统采用SVM模型进行问题分类，结合余弦、Jaccard等相似度度量方法。系统在自动化评估中达到95%的F1分数，人类满意度平均为0.9分。
BanglaMed-QA: A Question Answering System for Healthcare Support in Bangla
Medical question answering (QA) systems have become crucial tools for providing reliable health information. But they remain very unexplored for low-resource languages like Bangla due to limited datasets and systems tailored to these languages. To address this, we introduce BanglaMed-QA, a robust QA system specifically designed for the Bangla medical domain. The process begins with building a structured medical knowledge base that includes 4,493 QA pairs in 9 categories under 506 diseases. To improve semantic comprehension, domain-specific root word dictionaries and synonym sets are proposed, in addition to part-of-speech tagging for anaphora resolution. We adopt supervised machine learning models in which SVM is found to be the best model to categorize questions. Multiple similarity metrics, including cosine, Jaccard, BM25, and Levenshtein, are applied with soft and hard voting methods for query matching. The performance of the QA system has been evaluated in two aspects, with a 95% F1 score in an automated evaluation and an average human satisfaction rating of 0.9 out of 1.0. This validates the real-world application of BanglaMed-QA in closing the healthcare information gap for Bangla speakers.